System and method for assessing suicide risk of a patient based upon non-verbal characteristics of voice data

1. A computer-implemented method for assessing suicide risk in a human subject, the method comprising the steps of: receiving voice data of the subject; and classifying the subject as suicidal or non-suicidal based upon a computerized analysis of one or more nonverbal characteristics of the voice data performed on a support vector machine (SVM) that utilizing hidden Markov models (HMMs), the nonverbal characteristics of the voice data including prosodic and voice quality-related features of the voice data selected from one or more of an acoustic measure of energy in dB (en, en slope ), an acoustic measure of fundamental frequency (f 0 ), an acoustic measure of peak slope (peak), and an acoustic measure of spectral stationarity (ss), the prosodic and voice quality features including an acoustic measure of Liljencrants-Fant (LF) model parameters from time domain estimation methods, including three time-based parameters (R a , R k and R g ) an amplitude parameter (EE), an Open Quotient (OQ), and a parameter characterizing the basic shape of the LF model (R d ) and the classifying including classifying the subject as being suicidal based upon the voice data exhibiting the characteristics of a breathy phonation type.

2. The method of claim 1 , wherein the characteristics of a breathy phonation type are determined based upon one or more acoustic features selected from the group consisting of: Open Quotient (OQ), Normalized Amplitude Quotient (NAQ) and peak slope (peak).

3. The method of claim 2 , wherein the characteristics of a breathy phonation type are determined based upon one or more acoustic features selected from relatively larger R k values and relatively smaller R g values.

4. A system for assessing suicide risk for a human subject, the system comprising at least one processor, and a non-transitory computer-readable medium containing instructions stored thereon which when executed by the at least one processor, perform a method of assessing suicide risk for the human subject, the method comprising: receiving voice data of the subject; and classifying the subject as suicidal or non-suicidal based upon a computerized analysis of one or more nonverbal characteristics of the voice data performed on a support vector machine (SVM) utilizing hidden Markov models (HMMs), the nonverbal characteristics of the voice data including prosodic and voice quality-related features of the voice data selected from one or more of an acoustic measure of energy in dB (en, en slope ), an acoustic measure of fundamental frequency (f 0 ), an acoustic measure of peak slope (peak), and an acoustic measure of spectral stationarity (ss), the prosodic and voice quality features including an acoustic measure of Liljencrants-Fant (LF) model parameters from time domain estimation methods, including three time-based parameters (R a , R k , R g ) an amplitude parameter (EE), an Open Quotient (OQ), and a parameter characterizing the basic shape of the LF model (R d ) and the classifying including classifying the subject as being suicidal based upon the voice data exhibiting the characteristics of a breathy phonation type.

5. The system of claim 4 , wherein the characteristics of a breathy phonation type are determined based upon one or more acoustic features selected from the group consisting of: Open Quotient (OQ), Normalized Amplitude Quotient (NAQ) and peak slope (peak) features.

6. The system of claim 5 , wherein the characteristics of a breathy phonation type are determined based upon one or more acoustic features selected from relatively larger R k values, and relatively smaller R g values.